Systems and methods for topographical characterization of medical image data

ABSTRACT

Computer-implemented methods are used to analyze a medical image to assess the state of the sample region. In at least one embodiment, the method comprises receiving at least one medical image collected previously from an image source, the at least one medical image comprising a plurality of voxels, each characterized by a signal value; classifying the signal value of each voxel as representing one of healthy tissue or diseased tissue based on a threshold value; and analyzing at least one topographical feature of the at least one medical image.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.62/349,985, entitled Systems and Methods for Spatial Characterization ofImage Data, and filed Jun. 14, 2016, which is incorporated herein byreference in its entirety. Further, this application relates to U.S.application Ser. No. 13/539,254, entitled Tissue Phasic ClassificationMapping System and Method, and filed Jun. 29, 2012; U.S. applicationSer. No. 12/395,194, issued as U.S. Pat. No. 9,289,140, entitled Systemsand Methods for Imaging Changes in Tissue, and filed Feb. 27, 2009; U.S.application Ser. No. 13/462,500, issued as U.S. Pat. No. 8,768,431,entitled Systems and Methods for Tissue Imaging, and filed May 2, 2012;U.S. application Ser. No. 13/683,746, issued as U.S. Pat. No. 9,053,534,entitled Voxel-Based Approach for Disease Detection and Evolution, andfiled Nov. 21, 2012, each of which is incorporated herein by referencein its entirety.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under grant CA085878awarded by the National Institutes of Health. The government has certainrights in the invention.

FIELD OF THE INVENTION

The present disclosure relates to novel and advantageous systems andmethods for monitoring tissue regions and, more particularly, to systemsand methods for characterizing tissue regions to determine diseaseseverity.

BACKGROUND OF THE INVENTION

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

Chronic obstructive pulmonary disease (COPD) is a highly andincreasingly prevalent disorder referring to a group of lung diseasesthat block airflow during exhalation and make it increasingly difficultto breathe. COPD is one cause of morbidity, mortality, and healthcarecost worldwide with an estimated global prevalence of approximately 12%of adults aged ≥30 years in 2010 and rising with the ageing population.COPD can cause coughing that produces large amounts of mucus, wheezing,shortness of breath, chest tightness, and other symptoms. Emphysema andchronic asthmatic bronchitis are two of the main conditions that make upCOPD. Cigarette smoking is one leading cause of COPD. Many people whohave COPD smoke or used to smoke. Long-term exposure to other lungirritants, such as air pollution, chemical fumes, or dust, also maycontribute to COPD. In all cases, damage to lung airways may eventuallyinterfere with the exchange of oxygen and carbon dioxide in the lungs,which can lead to bodily injury. COPD is generally identified by airwaylimitations that may arise from progressive emphysematous lungdestruction, small airways disease, or a combination of both. COPD is aheterogeneous disorder that can arise from pathological processesincluding emphysematous lung tissue destruction, gross airway disease,and functional small airways disease (fSAD) in varying combinations andseverity within an individual patient. It is generally accepted thatfSAD and emphysema are two main components of COPD and that a spectrumof COPD phenotypes with varying contributions of these two componentsexists in individual patients. Recent reports found that COPD etiologyvaries among populations, including risk factors associated with tobaccosmoke, cooking fuels, environmental pollution and family genetics. Thishas led to the current understanding that COPD covers a spectrum ofpathophysiologies.

Given the high prevalence and clinical cost of COPD, there is a need forfurther advancements to enable COPD phenotypes and therapy response tobe quantified. Beyond COPD, small airway obstruction is a primarymanifestation in various other lung diseases, including asthma,obliterative bronchiolitis, and cystic fibrosis. Venegas, J. G., et al.Self-organized patchiness in asthma can represent a prelude tocatastrophic shifts. Nature 434, 777-782 (2005). Some have recentlyexplored the importance of disease heterogeneity and local interactionbetween neighboring structures using model simulations of asthma. Theyhave shown that small heterogeneity in ventilation potential produces animbalance in the system leading to large patched effects, termedself-organized clustering.

Numerous techniques have been used in attempting to measure COPD,including several imaging techniques. Computer tomography (CT) is aminimally invasive imaging technique that is capable of providing bothhigh contrast and detailed resolution of the pulmonary systems and thathas been used to aid physicians in identifying structural abnormalitiesassociated with COPD. Although CT is primarily used qualitatively (i.e.,through visual inspection), research has been devoted to the applicationof quantitative CT, measured in Hounsfield Units (HU) for identifyingunderlying specific COPD phenotypes, with the hopes that suchquantitative techniques would dictate an effective treatment strategyfor the patient. Knowing the precise COPD phenotype for an individualpatient, including the location, type, and severity of damage throughoutthe lungs would allow for the formulation of a tailored treatmentregimen that accounts for the patient's specific disease state.

Clinical presentation and monitoring of COPD have been describedprimarily through spirometry as pulmonary function measurements, such asforced expiratory volume in one second (FEV1). Although highlyreproducible, these measurements assess the lungs as a whole and areunable to differentiate two important components of COPD: emphysema andsmall airways disease. In addition, spirometry does not provide spatialcontext for regional heterogeneity of these components. X-ray computedtomography (CT) has addressed some of these limitations by allowingclinicians to verify emphysema in patients exhibiting loss of pulmonaryfunction. Even with these techniques, COPD is often undiagnosed in earlystages, impeding proper treatment with the disease potentiallyprogressing to permanent lung damage (i.e. emphysema). Although COPDphenotyping has been prolifically reported in the literature, lack ofaccurate diagnostic tools have hampered the development of effectivetherapies. Nevertheless, significant advances in technologies areproviding physicians opportunities to shift towards more effective,localized therapies.

Various strategies have been undertaken to identify metrics that moreaccurately assess COPD subtypes, such as genetic, molecular and cellularmarkers as well as medical imaging devices and methodologies. Althoughadvances in biological phenotyping have shown promise in identifyingdisease heterogeneity in patients, these approaches are generally eitherglobal measures or highly invasive. In contrast, medical imagingprovides clinicians with a relatively non-invasive and reproducibleapproach that provides functional information that is spatially defined.

A variety of CT-based metrics have been evaluated separately oninspiratory and expiratory CT scans or in combination. One metric thatmay be used is the lung relative volume of emphysema known as LowAttenuation Areas (LAA), which is determined by the sum of all imagevoxels with HU<−950 normalized to total inspiratory lung volume on aquantitative CT scan. This metric may be calculated using standardimaging protocols making it readily measurable at clinical sites forevaluation, and the LAA approach has been validated by pathology.However, this metric only identifies a portion of one component (i.e.emphysema) of the spectrum of underlying COPD phenotypes discussedabove. Nevertheless, the validation of LAA has prompted researchers toinvestigate the utility of inspiratory and expiratory CT scans, eitheranalyzed individually as with LAA or in unison, to identify imagingbiomarkers that provide for a more accurate correlate of COPD.

Although various instruments (e.g. PET, SPECT and MRI) are heavilyinvestigated as surrogates of clinical outcome, CT, with its highresolution and lung contrast, continues to be the most widely usedmedical imaging device in the clinic. As such, advances in thistechnology are likely to have an important impact on patient care. CTcan be considered a quantitative map, where attenuation scans areapproximated as linearly proportional to tissue density, represented asHounsfield units (HU). Extensive research in CT image post-processinghas generated an array of potentially diagnostic and prognosticmeasures. Filter-based techniques and airway wall measurements have beenalso been used. Although these methodologies have advanced understandingof COPD, many have found limited use in the clinic due to concerns aboutcost and radiation exposure. Nevertheless, the quantification ofdiscrete phenotypes of emphysema using CT has had an impact on patientcare. At present at least three emphysema patterns (i.e., centrilobular,panlobular, and paraseptal emphysema) have been identified, each ofwhich are strongly associated with a range of respiratory physiologiesand functional measures. The understanding that spatial patterns ofemphysema serve as indicators of COPD subtypes has spawned progress inlobe segmentation algorithms as well as the need to evaluate topologicalfeatures.

BRIEF SUMMARY OF THE INVENTION

The present disclosure, in one or more embodiments, relates to acomputer-implemented method of analyzing a medical image to assess thestate of a sample area or region of tissue. The method may comprisereceiving a medical image containing the sample area or region oftissue, wherein the medical image was obtained from a medical imagingdevice, where the medical image comprises a plurality of voxels, eachcharacterized by a signal value; and then calculating at least onetopographical feature of the medical image. The medical image may beselected from the group consisting of a phasic classification map, aparametric response map, a diffusion image, a perfusion image, apermeability image, a normalized image, a spectroscopic image, a kineticparameter map and a quantified image. The medical imaging device may beselected from the group consisting of magnetic resonance imaging (MRI),computed tomography (CT), two-dimensional planar X-ray (either plainfilm converted to digital images, or digital X-ray images), X-raymammography, positron emission tomography (PET), ultrasound (US), orsingle-photon emission computed tomography (SPECT). In some embodiments,the topographical features may be selected from the group consisting ofsurface area, mean curvature length, the Euler-Poincare characteristicand a condensed descriptor of aggregation.

Additionally, the present disclosure, in one or more embodiments,relates to a computer-implemented method of analyzing a sample region oftissue, for example a lung, with dynamic bodily movement (e.g.inhalation and exhalation) to determine the condition of the sampleregion. The method may, in at least one embodiment, comprise receiving,using a medical imaging device, a first image data set of the sampleregion at a first position or during a first bodily movement, the firstimage data set comprising a first plurality of voxels each characterizedby a signal value; and receiving, using the medical imaging device, asecond image data set of the sample region at a second position orduring a second bodily movement, the second image data comprising asecond plurality of voxels each characterized by a signal value in thesecond image data set. The method may further comprise deformablyregistering the first image data set and the second image data set toproduce a co-registered image data set that comprises a plurality ofco-registered voxels, wherein each of the co-registered voxels includesthe signal value of the voxel associated with the first image data set,and the signal value of the voxel from the second image data set. Athreshold analysis may then be performed on the co-registered voxels toidentify co-registered voxels indicating the presence or absence ofdamaged tissue. For example, with lung tissue, co-registered voxels witha first signal value of greater than a threshold value may indicate theabsence of emphysematous tissue, and a second signal value of less thana threshold value may indicate the presence of air-trapping,non-emphysematous tissue. The method may further comprise performing atopographical feature analysis of the emphysematous tissue and/orperforming a topographical feature analysis of the air-trapping,non-emphysematous tissue. In some embodiments, the topographical featureanalysis quantifies features selected from the group comprising ofsurface area, mean curvature length, the Euler-Poincare characteristicand a condensed descriptor of aggregation.

Further, the present disclosure, in one or more embodiments, relates toa computer-implemented method for analyzing a parametric response map ora phasic classification map. The method may comprise receiving a firstset of parametric measurement data for a tissue region, the first set ofparametric measurement data comprising a plurality of voxels; andreceiving one or more subsequent sets of parametric measurement data,each subsequent set of parametric measurement data comprising aplurality of voxels. The method may further comprise registering the oneor more subsequent sets of parametric measurement data with the firstset of parametric response data. At least one classification of at leastone of the plurality of voxels may then be performed within the tissueregion, on a voxel-by-voxel basis, based on the change in parametricmeasurement data, using a defined threshold for change, wherein each ofthe changes is determined by comparing voxels of the one or moresubsequent sets of parametric measurement data to the voxels on one ormore subsequent sets of parametric measurement data obtained previouslyor to the voxel of the first set of parametric measurement data. Themethod may still further provide performing a topographical featureanalysis of the voxels with increased parametric measurement propertiesand/or a topographical feature analysis of at least one of theclassifications. In some embodiments, the topographical feature analysismay quantify at least one of the topographical features, includingsurface area, mean curvature length, the Euler-Poincare characteristic,and/or a condensed descriptor of aggregation. In some embodiments, theparametric measurement data may be selected from the group consisting ofa phasic classification map, a parametric response map, a diffusionimage, a perfusion image, a permeability image, a normalized image, aspectroscopic image, a kinetic parameter map and/or a quantified image.

BRIEF DESCRIPTION OF THE DRAWINGS

While the specification concludes with claims particularly pointing outand distinctly claiming the subject matter that is regarded as formingthe various embodiments of the present disclosure, it is believed thatthe invention will be better understood from the following descriptiontaken in conjunction with the accompanying Figures, in which:

FIG. 1 is a flow chart of a method of analyzing one or more images, inaccordance with at least one embodiment of the present disclosure.

FIGS. 2A-2B are examples of images retrieved from an imaging source,according to the exemplary method of FIG. 1.

FIGS. 3A-3C are examples of the parametric response maps generated fromthe images of FIGS. 2A-2B, according to the exemplary method of FIG. 1.

FIG. 4 is an example of the topographical feature analysis of at leastone of the parametric response maps shown in FIGS. 3A-3C, according tothe exemplary method of FIG. 1.

FIGS. 5A-5B are examples of images and parametric response maps for twopatients, in accordance with at least one embodiment of the presentdisclosure.

FIGS. 6A-6B are examples of topographical feature analysis of parametricresponse maps for the two patients shown in FIGS. 5A-5B, in accordancewith at least one embodiment of the present disclosure.

DETAILED DESCRIPTION

The present disclosure relates to computer-implemented systems andmethods for assessing a variety of tissue states using a phasicclassification map (PCM) analysis or a parametric response map (PRM)analysis of quantitative medical image data. Although the disclosureherein is discussed with respect to medical images or medical image dataretrieved from one or more PCMs or PRMs, the medical images may be adiffusion image, a perfusion image, a permeability image, a normalizedimage, a spectroscopic image, a quantified image, and other suitablemedical images. Likewise, the medical image data may be retrieved from adiffusion image, a perfusion image, a permeability image, a normalizedimage, a spectroscopic image, a quantified image, and other suitablemedical images.

In some embodiments, the systems and methods of the present disclosuremay use deformation registration of medical image data, comparingmedical images taken at different tissue states, in some casestemporally, from which a voxel-by-voxel, or pixel-by-pixel, imageanalysis may be performed. The analysis may be based on acomputer-implemented algorithm that compares the patient's medicalimage(s) to one of more medical image(s) of the same or similar tissueregions from the same patient, which may have been obtained earlier, orfrom one or more other individuals for whom the corresponding healthstatus and/or outcomes are known.

The medical image(s) or medical image data for the patient's image(s)may be from a variety of different imaging sources, including, but notlimited to magnetic resonance imaging (MRI), computed tomography (CT),two-dimensional planar X-ray (either plain film converted to digitalimages, or digital X-ray images), X-ray mammography, positron emissiontomography (PET), ultrasound (US), or single-photon emission computedtomography (SPECT). Within a given imaging source (i.e. MRI, CT, X-Ray,PET, and SPECT), a variety of data may be generated. For example, MRIdevices may generate diffusion, perfusion, permeability, normalized andspectroscopic images, which includes molecules containing, for example,but not limited to 1H, 13C, 23Na, 31P, and 19F, hyperpolarized Helium,Xenon and/or 13C MRI, which may also be used to generate kineticparameter maps. PET, SPECT, and CT devices are also capable ofgenerating static images as well as kinetic parameters by fittingtemporally resolved imaging data to a pharmacokinetic model. Imagingdata, irrespective of source and modality, may presented as quantified(i.e., made to have physical units) or normalized (i.e., images scaledso that the pixel intensities fall within a known range based on anexternal phantom, something of known and constant property, or a definedsignal within the image volume) maps so that images may be comparedbetween patients as well as data acquired during different scanningsessions.

Imaging data or sets of images or imaging data may be acquired for atissue region at different times and/or at different phase states ofmovement. For example, in some embodiments, a first set of image datamay be acquired prior to a treatment, and a second set of image data maybe acquired after administration of the treatment. As another example,in some embodiments, a first set of image data may be acquired during afirst phase state of movement, such as lung inspiration, and a secondset of image data may be acquired during a second phase state ofmovement, such as lung expiration. As yet another example, a first setof image data may be acquired during a first phase state of movement,such as flexion of a muscle, and a second set of image data may beacquired during a second phase state of movement, such as extension of amuscle.

The systems and methods of the present disclosure are not limited to aparticular type or kind of tissue region or a particular type of motionor movement. By way of example only, suitable tissue types include lung,prostate, breast, colon, rectum, bladder, ovaries, skin, liver, spine,bone, pancreas, cervix, lymph, thyroid, spleen, adrenal gland, salivarygland, sebaceous gland, testis, thymus gland, penis, uterus, trachea,skeletal muscle, smooth muscle, heart, brain, and other tissue types. Insome embodiments, the tissue region may be a whole body or large portionthereof (for example, a body segment such as a torso or limb; a bodysystem such as the gastrointestinal system, endocrine system, etc.; or awhole organ comprising multiple tumors, such as whole liver) of a livinghuman being. In some embodiments, the tissue region may be a diseasedtissue region. In some embodiments, the tissue region may be an organ.In some embodiments, the tissue region may be a tumor, for example, amalignant or benign tumor. In some embodiments, the tissue region is abreast tumor, a liver tumor, a bone lesion, and/or a head/neck tumor. Insome embodiments, the tissue may be from a non-human animal. By way ofexample only, suitable movements may include respiratory and cardiaccycle movements, smooth and striated muscle contraction, joint andspinal positioning for assessment by Dynamic-Kinetic MRI and positionalMRI, and induced propagated waves at varying frequencies in tissues ortumors assessed by magnetic resonance elastography.

In addition, the systems and methods of the present disclosure are notlimited to a particular type or kind of treatment. In some embodiments,the systems and methods of the present disclosure may be used as part ofa pharmaceutical treatment, a vaccine treatment, a chemotherapy basedtreatment, a radiation based treatment, a surgical treatment, ahomeopathic treatment, or a combination of treatments. In otherembodiments, the systems and methods of the present disclosure may beused for screening for disease, prognosis or diagnosis of diseases,base-line assessments, treatment planning, treatment follow-up, or othereducation regarding tissue state.

In previously known methods of analysis, images are generallyinterpreted subjectively based on image contrast, or quantitatively bylesion size, for example. In contrast, embodiments of the systems andmethods of the present disclosure may objectively depict the imagecontrast change over time in normal and diseased tissue for use as apotential surrogate indicator of disease evolution, for example,response or progression.

In some embodiments, the method of the present disclosure may includeobtaining at least two volumetric medical images via an imagingmodality; registering the image(s) to a reference image set; segmentingthe voxel-by-voxel differences relative to a specified significancethreshold; quantifying the volume of voxels that exceed an establishedthreshold or amount of change; and/or producing at least one classifiedor colorized parametric response map (PRM) of one or more tissue regionsthat exhibit significant change. Embodiments of the present disclosuremay include more, fewer, or different steps. In some embodiments,standard non-quantitative images, such as standard MRI usingconventional whole tissue/volume statistical approaches (for example,but not limited to mean, median, skewness, percentile, kurtosis,Kullback-Leibler, quantiles, standard deviation, etc.), may be used tocreate a PRM, after the images have been normalized in accordance withembodiments of the present disclosures.

In some embodiments, the systems and methods of the present disclosurerelate to automatically retrieving an objective description of thespatial characteristics of PRM categories indicating, for example,increased, decreased or stable areas of cerebral blood volume (“CBV”).Examples of spatial characteristics of PRM categories include, but arenot limited to, statistically significant increased parametric values,statistically significant decreased parametric values, statisticallystatic parametric values, parametric values above one threshold in oneimage and below another threshold in another image, parametric valuesabove one threshold in one image and above another threshold in anotherimage, or parametric values below one threshold in one image and belowanother threshold in another image. Examples of other conditions orfeatures indicated by the spatial characteristics of PRM categoriesinclude, but are not limited to, an apparent diffusion coefficient(ADC), capillary permeability (Ktrans), and standard uptake value (SUV),among others. In other embodiments, spatial characteristics of PRMcategories resulting from other parametric imaging data can be analyzedthat include but are not limited to, apparent diffusion coefficients(ADC), pharmacokinetic parameters (e.g. K_(trans)) or standard uptakevalue (SUV). Advantageously, embodiments of the present disclosure mayuse conventional non-quantitative (i.e. conventional “weighted”) imagesthat are normalized and subsequently analyzed. Prior to analysis, insome embodiments, linear or deformable algorithms may be applied to theimages to spatially align them. In other words, the images that areobtained at different scan intervals are aligned with the referenceimage. Objective analysis of the topographical properties of PRMcategories can provide for detailed insight into the status, extent,progression and response of a disease using images obtained from any ofthe imaging sources and imaging modalities discussed above.

At least some embodiments of the present disclosure may use avoxel-based PRM approach to provide for the early detection and spatialdepiction of, for example, tumor progression prior to detection bycurrently available conventional MRI-based criteria, as described, forexample, in U.S. application Ser. No. 13/683,746, issued as U.S. Pat.No. 9,053,534, entitled Voxel-Based Approach for Disease Detection andEvolution, and filed Nov. 21, 2012, which is incorporated by referenceherein in its entirety. Other extensions of the systems and methods ofthe present disclosure include applications using many other weightedimage data types, such as, but not limited to T1, T2, proton densityimages, FLAIR and STIR (inversion recovery pulses), metabolite-specificimages, pulsed gradient spin echo images (PGSE), and/or oscillatinggradient spin echo (OGSE) for MRI that when normalized are used tomonitor changes associated from a multitude of disease types and acrossall tissue types over time. Normalized data generated from other medicalimaging devices (e.g. optical, CT, X-Ray, PET, and SPEC) are alsoapplicable.

Additionally, in some embodiments, the present disclosure relates tospatial characteristics of imaging data. The spatial characteristics maybe the same as the PRM categories discussed above, but the data beinganalyzed are, in some embodiments, very different. With respect to thespatial characteristics of PRM categories, the spatial characteristicsof some change in the images may be reviewed, according to someembodiments. These may be with respect to multiple images. In the caseof the spatial characteristics of imaging data, the spatialcharacteristics of only one single image are being reviewed, accordingto some embodiments. For example, in some embodiments, the presentdisclosure relates to classifying COPD phenotypes by their topologicalproperties. Utilizing component classification maps derived from PRM orPCM, disease patterns may be extracted and quantified to generateimaging surrogates of relevant clinical outcome measures. Although othermethods have evaluated PRM and PCM as a quantitative index of diseasetype and extent, spatial context of PRM and PCM has been underutilized.For example, topological evaluation of COPD components, as derived byPCM, may lead to better-informed clinical care and provide furtherinsight into the heterogeneous clinical subtypes of COPD. Parametersthat represent different topological features of PRM and PCMclassification maps may include: surface area (S), mean curvature length(B), the Euler-Poincare characteristic (χ), and a condensed descriptorof aggregation (α). Additional parameters that represent differenttopological features may include volume, mean breadth, perimeter, andgenus.

In some embodiments, the methods of the present disclosure may use avoxel-by-voxel, or pixel-by-pixel, PCM analysis for assessing tissuestates, such as COPD severity in lung tissue, or other tissue states ofthe lung or other tissue that may be associated with other conditions ordiseases. PCM may generally be considered a particular application ofanother voxel-based method, the parametric response map (PRM). PRM wasdeveloped and shown to improve the sensitivity of diffusion-MRI data toaid in identifying early therapeutic response in glioma patients. PRM,when applied to diffusion-MRI data, had been validated as an earlysurrogate imaging biomarker for gliomas, head and neck cancer, breastcancer and metastatic prostate cancer to the bone, for example. Inaddition, PRM has been applied to temporal perfusion-MRI for assessingearly therapeutic response and survival in brain cancer patients. PRM isfound to improve the sensitivity of the diffusion and perfusion MRI databy classifying voxels based on the extent of change in the quantitativevalues over time. This approach provides not only spatial informationand regional response in the disease state to treatment but is also aglobal measure that can be used as a decision making tool for thetreatment management of patients. The global measure is presented as therelative volume of tumor whose quantitative values have increased,decreased or remained unchanged with time. As used herein, PCM may beconsidered a particular application of PRM as applied to cyclic imagedata. Throughout this application, the methods or systems of the presentdisclosure may be referred to as either PRM or PCM.

The systems and methods of the present disclosure may be sensitiveenough to detect varying tissue states, from a normal state through to adiagnosable pathology condition, for example. There are generally atleast three steps in applying PRM or PCM prior to clinical diagnosis,including: image acquisition from an imaging source such as CT,co-registration and other image processing, and classification of thevoxels making up the processed image by comparing a signal value of thevoxel to one or more threshold values. Various classification schemesare contemplated and within the spirit and scope of the presentdisclosure. In some embodiments, the classification scheme can includecolor-coded voxels of the processed images that form the PCM. Forexample, in some cases, the classification system may include colorcoded voxels representing healthy lung parenchyma, that is normal lungtissue, the color green; color coding voxels representing tissueexhibiting functional small airways disease (fSAD), the color yellow;and color coding voxels representing emphysema, the color red. It willbe understood that the color coding scheme could be any suitable colorcoding scheme and may employ any suitable or desirable colors. In otherembodiments, a classification scheme may comprise, for example, insteadof using colors, varying shades of gray may be used to denote thedifferent classifications. Further, in some embodiments, aclassification scheme may comprise different geometric shapes could beused, for example open circles and closed circles, or any other suitablemethod for denoting differences between individual voxels on aparametric response map may be used.

In contrast to conventional CT-based quantitative measures, the systemsand methods of the present disclosure may use deformable registration toalign images of different phases of the respiratory cycle, specificallyat inspiration and expiration. Deformable registration locally warps oneimage so that its features and structures align with at least one otherimage or previous images that have been subject to deformableregistration. Deformable registration occurs in such a way that volumeis not necessarily preserved. The systems and methods of the presentdisclosure may identify unique signatures of disease extent where localvariations in lung function are classified based on a voxel-by-voxelcomparison of a signal value indicating lung density, as measured inHounsfield Units (HU), from co-registered scans acquired duringinspiratory and expiratory cycles to provide a global measure as well asa local measure of COPD severity. These local variations may bedetermined by taking two or more images acquired at different phases ofmovement, and/or at different times, and performing deformableregistration on the images, from which clinically meaningful data may beextracted and used in diagnostic and prognostic treatments. In someexamples, numerous thresholds may be applied to the different phaseimages, offering a 2, 3, 4 color (or more) set of images andcorresponding metrics. The result is a technique by which PCM may beused as a prognostic imaging biomarker of disease, using conventionalimaging protocols (CT, MRI, etc.) acquired at varying physiologicalstates of the lung. While the difference in signal values ofco-registered voxels is described herein as important and providinginformation that may be used in the PCM techniques described herein, itis also contemplated that in some embodiments it is not only thedifference between signal values of voxels from serial images that mayconvey information, but the initial value or baseline value that mayalso convey meaningful information and may be incorporated intoembodiments of the present disclosure.

While some examples provided herein disclose the collection of twoimages (for example, one image taken at inspiration and one taken atexpiration for the purposes of characterizing and assessing lungtissue), it is also contemplated and within the spirit and scope of thepresent disclosure, that multiple images may be collected and used togenerate a PCM or a PRM. For example, in an embodiment of the presentdisclosure, PCM may be used to classify and assess the state of cardiactissue. Accordingly, multiple images, for example from two to at leastfourteen images, may be taken throughout a cardiac cycle and used tocreate a PCM to assess cardiac tissue.

Different methods have been proposed for quantifying spatial patternsand heterogeneity, including fractal analysis, variograms, lacunarityanalysis, and Minkowski functionals (MF), of which many have been usedto study lung diseases. In some embodiments, Minkowski functionals maybe particularly useful as open source algorithms are readily availableand may be applied to an entire object or computed locally to retainspatial information. As a sensitive measure of diffuse disease vs.aggregated disease, these functions (i.e. S, B, χ and α) may be executedon PRM classification maps, to reveal unique spatial patterns ofdisease, emphysematous and non-emphysematous air trapping for example,as indicators of meaningful clinical measures. By incorporatingphenotypic information obtained by PRM with spatial patterns bytopological analyses, information within paired CT data may beevaluated.

Extensive work has been performed for stratification of diseasephenotypes through analysis of emphysema patterns. Many of these studieshave concluded that diffuse emphysema patterns are indicative of anaccelerated decline in lung function. Because identification of thenon-emphysematous component has only recently been attainable, little isknown about the effect of its spatial distribution on clinical outcomes.Wide variations in PRM-derived fSAD distributions are known to existfrom qualitative observations, and fSAD heterogeneity, through S, may besignificantly correlated with clinical measures and may providecomplementary insight into the disease than what is attainable throughdisease extent alone.

An important feature of the systems and methods of the presentdisclosure is the retention of spatial information from the original PRMclassification maps, which is generally a time-consuming process. In atleast one embodiment, to reduce processing time while maintainingsufficient spatial information, local determination of topologic indicescan be performed using a gridded analysis where a first moving windowoverlaps a second window, each window with sufficient local imageinformation to adequately describe the local metric behavior. Theresults may be affected by the selection of such parameters as gridspacing and kernel size and shape. The sensitivity of each topologicalparameter may vary based on the process of local analysis.

The ability of the systems and methods of the present disclosure toretain spatial context of local topology may help focus clinicians onspecific disease-driving tissue regions. More specifically, the systemsand methods of the present disclosure may also aid in the targeting ofhigh risk lung regions for more-invasive interventions such as airwaybrushing, lavage, and biopsy, reducing sampling error.

The systems and methods of the present disclosure further includeextracting topological features from PRM for spatial characterization ofCOPD phenotypes. In at least one embodiment shown in FIGS. 1-6, the fSADpattern may be used as a key characteristic for assessing diseaseseverity.

FIG. 1 shows an example of at least one method 100 of the presentdisclosure, receiving at least one medical image from an imaging source,as shown at 102; creating a PCM or PRM having a plurality of voxels, asshown at 104; classifying the voxels of the PCM or PRM, as shown at 106;and performing a topographical feature analysis as shown at 108. Asshown in FIG. 2A, an image of a lung from at least one COPD patientduring expiration may be received from an imaging source such as CT, andas shown in FIG. 2B, an image of a lung from at least one COPD patientduring inspiration may be received from the imaging source. In at leastone embodiment, lung parenchyma and airways can be segmented from thethoracic cavity to restrict image registration and analysis to lungparenchyma. In some embodiments, a filter such as a de-noising filtermay be applied to the at least one image to reduce noise or other imagedistortion. In at least one embodiment, a 2D median filter may be usedon each axial slice with a moving window of 32 voxels in order tomitigate the effect of noise on resulting spatial maps. In someembodiments, two or more images may then be spatially aligned using anautomated algorithm. For example, the image during inspiration may bealigned with the image during expiration.

A PCM or a PRM comprising multiple voxels may then be created from theimage(s), and an analysis performed on the PCM, PRM, or other parametricmeasurement data wherein the voxels may be classified according to oneor more classifications based on at least one threshold. In at least theembodiment shown in FIGS. 3A-3C, voxels may classified into one of threeclassifications by imposing two thresholds: (i) −950 HU on inspiratoryCT and (ii) −856 HU on expiratory CT. The classifications have beenpreviously reported to identify healthy lung parenchyma (PRM^(Normal),green; >−950 HU on inspiration and >−856 HU on expiration), functionalsmall airways disease (PRM^(fSAD), yellow; >−950 HU on inspiration and≤−856 HU on expiration), and emphysema (PRM^(Emph), red; ≤−950 HU oninspiration and ≤−856 HU on expiration). Global measures from PRManalysis may be reported as the relative lung volume for eachclassification. In order to minimize the contribution of blood vesselsand airways in the analysis, all voxels with HU values >−500 HU ineither scan may be omitted. FIGS. 3A-3C show examples of a PCM or a PRMcreated from the images of FIGS. 2A-2B with voxels classified as normal,healthy lung parenchyma (FIG. 3A), voxels classified as fSAD (FIG. 3B),and voxels classified as emphysema.

A topographical feature analysis may then be performed for the PCM orPRM using at least one topographical feature, such as surface area (S).At least four parameters that represent different topological featuresof PRM classification maps can be analyzed with the methods of thepresent invention: surface area (S), mean curvature length (B), theEuler-Poincare characteristic (χ), and a condensed descriptor ofaggregation (α). These parameters may be determined locally, oversub-volumes of the lung resulting in a parameter map, or globally, overthe entire lung volume resulting in a parameter scalar quantity. In someembodiments, S may be preferred due to its high correlation withclinical measures and its stability between local and globalevaluations. This measure is indicative of distribution heterogeneity,with higher values indicating a more diffuse disease and lower valuesindicating a more clustered pattern. Topological properties of each PRMclassification map may explored as independent indicators of clinicaloutcome. These topological properties may be defined through theMinkowski measures associated with 3D distributions: Volume (V, in mm³),Surface Area (S, in mm²), Mean Breadth (B, in mm), and theEuler-Poincaré statistic (χ). Additional processing with use of the χstatistic may produce a condensed descriptor of aggregation, α. Maps ofMinkowski measures (i.e. V, S, B, χ and α) may, in one embodiment, becomputed using a moving window evaluated on a grid with 4-voxel spacing.Local values from each parameter may be normalized to produce parametricdensities, with V, S, and B normalized by the masked local window volumeand χ and α normalized by the masked window voxel count. In someembodiments, for display purposes, the Minkowski measures (S, B, χ andα) may be multiplied by the local density, V, to highlight regions ofsubstantial disease. Final displayed representations of spatiallyresolved indices may, in some embodiments, be linearly interpolated backto original dimensions. In addition, global values for V, S, B, χ and αmay be calculated for each PRM classification over the entire lungvolume. The ability of multiple volume fractions to translate to thesame topological measure could be an important factor in the use ofindividual measures to provide meaningful results. FIG. 4 shows anexample of the analysis performed for the topographical feature S for aCOPD patient. As a result of this analysis on COPD patients, it wasdetermined that information on disease pattern is independent of diseaseextent, demonstrating that fSAD features as defined by PRM are keydisease characteristics that are strongly correlated with clinicaloutcome measures.

The systems and methods of the present disclosure are advantageousbecause of their ability to delineate disease pattern that resideswithin the PRM classification maps independent of the extent of disease(i.e. PRM^(fSAD)). For example, the pattern of fSAD, using the systemsand methods in the present disclosure for extraction of topologicalfeatures from PRM classification maps, is strongly correlated withclinical readouts even when considering the overall extent of thedisease (i.e. PRM). FIGS. 5A-5B show examples of medical images and PRMfor one patient with diffuse disease (FIG. 5A) and one patient withaggregated disease (FIG. 5B). Each of FIG. 5A-5B shows a first medicalimage 502 during inspiration, a second medical image 504 duringexpiration, a PRM 506 with voxels classified as fSAD, and a PRM 508 witha topographical feature analysis performed using S for each of thepatients. Although the two patients in FIGS. 5A-5B had near identicalspirometry readouts, the PRMs 506, 508 display differing spatialdistributions. As shown in FIGS. 6A-6B, spatial maps of all topologicalmeasures were generated for the two representative cases shown in FIG.5. FIGS. 6A-6B each show a PRM 602 displaying a topographical featureanalysis for measure V, a PRM 604 displaying a topographical featureanalysis for measure S, a PRM 606 displaying a topographical featureanalysis for measure B, a PRM 608 displaying a topographical featureanalysis for measure x, and a PRM 610 displaying a topographical featureanalysis for measure a. The PRMs displayed in FIGS. 6A-6B may bemultiplied by local relative volumes to highlight regions of interest,where fSAD may be prevalent. As shown in FIGS. 6A-6B, clear differencesin the spatial pattern of each measure are apparent between subjects,revealing signatures of disease distribution that may be clinicallyrelevant.

While some embodiments of the present disclosure have been describedwith respect to CT imaging of lung tissue for COPD patients and spatialcharacteristics of COPD phenotypes, it is to be appreciated that thesystems and methods of the present disclosure may be applicable andbeneficial with respect to a variety of imaging modalities, tissueregions, diseases or conditions, and clinical settings. That is, systemsand methods of the present disclosure may provide an analysis of spatialcharacteristics with respect to any suitable PRM or PCM-derivedcomponent classification map of any suitable tissue region. For example,a PRM classification map of brain tissue, showing for example theprogression of a brain tumor, may be analyzed similar to the abovedescribed examples in order to obtain spatial characteristic data forthe brain tissue. Systems and methods of the present disclosure mayreveal unique spatial patterns of disease or tumor progression, forexample, as an indicator of a meaningful clinical measure.

For purposes of this disclosure, any system described herein may includeany instrumentality or aggregate of instrumentalities operable tocompute, calculate, determine, classify, process, transmit, receive,retrieve, originate, switch, store, display, communicate, manifest,detect, record, reproduce, handle, or utilize any form of information,intelligence, or data for business, scientific, control, or otherpurposes. For example, a system or any portion thereof may be a personalcomputer (e.g., desktop or laptop), tablet computer, mobile device(e.g., personal digital assistant (PDA) or smart phone), server (e.g.,blade server or rack server), a network storage device, or any othersuitable device or combination of devices and may vary in size, shape,performance, functionality, and price. A system may include randomaccess memory (RAM), one or more processing resources such as a centralprocessing unit (CPU) or hardware or software control logic, ROM, and/orother types of nonvolatile memory. Additional components of a system mayinclude one or more disk drives or one or more mass storage devices, oneor more network ports for communicating with external devices as well asvarious input and output (I/O) devices, such as a keyboard, a mouse,touchscreen and/or a video display. Mass storage devices may include,but are not limited to, a hard disk drive, floppy disk drive, CD-ROMdrive, smart drive, flash drive, or other types of non-volatile datastorage, a plurality of storage devices, or any combination of storagedevices. A system may include what is referred to as a user interface,which may generally include a display, mouse or other cursor controldevice, keyboard, button, touchpad, touch screen, microphone, camera,video recorder, speaker, LED, light, joystick, switch, buzzer, bell,and/or other user input/output device for communicating with one or moreusers or for entering information into the system. Output devices mayinclude any type of device for presenting information to a user,including but not limited to, a computer monitor, flat-screen display,or other visual display, a printer, and/or speakers or any other devicefor providing information in audio form, such as a telephone, aplurality of output devices, or any combination of output devices. Asystem may also include one or more buses operable to transmitcommunications between the various hardware components.

One or more programs or applications, such as a web browser, and/orother applications may be stored in one or more of the system datastorage devices. Programs or applications may be loaded in part or inwhole into a main memory or processor during execution by the processor.One or more processors may execute applications or programs to runsystems or methods of the present disclosure, or portions thereof,stored as executable programs or program code in the memory, or receivedfrom the Internet or other network. Any commercial or freeware webbrowser or other application capable of retrieving content from anetwork and displaying pages or screens may be used. In someembodiments, a customized application may be used to access, display,and update information.

Hardware and software components of the present disclosure, as discussedherein, may be integral portions of a single computer or server or maybe connected parts of a computer network. The hardware and softwarecomponents may be located within a single location or, in otherembodiments, portions of the hardware and software components may bedivided among a plurality of locations and connected directly or througha global computer information network, such as the Internet.

As will be appreciated by one of skill in the art, the variousembodiments of the present disclosure may be embodied as a method(including, for example, a computer-implemented process, a businessprocess, and/or any other process), apparatus (including, for example, asystem, machine, device, computer program product, and/or the like), ora combination of the foregoing. Accordingly, embodiments of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, middleware, microcode,hardware description languages, etc.), or an embodiment combiningsoftware and hardware aspects. Furthermore, embodiments of the presentdisclosure may take the form of a computer program product on acomputer-readable medium or computer-readable storage medium, havingcomputer-executable program code embodied in the medium, that defineprocesses or methods described herein. A processor or processors mayperform the necessary tasks defined by the computer-executable programcode. Computer-executable program code for carrying out operations ofembodiments of the present disclosure may be written in an objectoriented, scripted or unscripted programming language such as Java,Perl, PHP, Visual Basic, Smalltalk, C++, or the like. However, thecomputer program code for carrying out operations of embodiments of thepresent disclosure may also be written in conventional proceduralprogramming languages, such as the C programming language or similarprogramming languages. A code segment may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, anobject, a software package, a class, or any combination of instructions,data structures, or program statements. A code segment may be coupled toanother code segment or a hardware circuit by passing and/or receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters, data, etc. may be passed, forwarded,or transmitted via any suitable means including memory sharing, messagepassing, token passing, network transmission, etc.

In the context of this document, a computer readable medium may be anymedium that can contain, store, communicate, or transport the programfor use by or in connection with the systems disclosed herein. Thecomputer-executable program code may be transmitted using anyappropriate medium, including but not limited to the Internet, opticalfiber cable, radio frequency (RF) signals or other wireless signals, orother mediums. The computer readable medium may be, for example but isnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, or device. More specificexamples of suitable computer readable medium include, but are notlimited to, an electrical connection having one or more wires or atangible storage medium such as a portable computer diskette, a harddisk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), acompact disc read-only memory (CD-ROM), or other optical or magneticstorage device. Computer-readable media includes, but is not to beconfused with, computer-readable storage medium, which is intended tocover all physical, non-transitory, or similar embodiments ofcomputer-readable media.

Various embodiments of the present disclosure may be described hereinwith reference to flowchart illustrations and/or block diagrams ofmethods, apparatus (systems), and computer program products. It isunderstood that each block of the flowchart illustrations and/or blockdiagrams, and/or combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer-executable programcode portions. These computer-executable program code portions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce aparticular machine, such that the code portions, which execute via theprocessor of the computer or other programmable data processingapparatus, create mechanisms for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.Alternatively, computer program implemented steps or acts may becombined with operator or human implemented steps or acts in order tocarry out an embodiment of the invention.

Additionally, although a flowchart may illustrate a method as asequential process, many of the operations in the flowcharts illustratedherein can be performed in parallel or concurrently. In addition, theorder of the method steps illustrated in a flowchart may be rearrangedfor some embodiments. Similarly, a method illustrated in a flow chartcould have additional steps not included therein or fewer steps thanthose shown. A method step may correspond to a method, a function, aprocedure, a subroutine, a subprogram, etc.

As used herein, the terms “substantially” or “generally” refer to thecomplete or nearly complete extent or degree of an action,characteristic, property, state, structure, item, or result. Forexample, an object that is “substantially” or “generally” enclosed wouldmean that the object is either completely enclosed or nearly completelyenclosed. The exact allowable degree of deviation from absolutecompleteness may in some cases depend on the specific context. However,generally speaking, the nearness of completion will be so as to havegenerally the same overall result as if absolute and total completionwere obtained. The use of “substantially” or “generally” is equallyapplicable when used in a negative connotation to refer to the completeor near complete lack of an action, characteristic, property, state,structure, item, or result. For example, an element, combination,embodiment, or composition that is “substantially free of” or “generallyfree of” an ingredient or element may still actually contain such itemas long as there is generally no measurable effect thereof.

We claim:
 1. A computer-implemented method of analyzing a medical imageto assess the state of the sample region, the method comprising:receiving a medical image collected previously from an image source, themedical image comprising a plurality of voxels, each characterized by asignal value; classifying the signal value of each voxel as representingone of a first disease state or a second disease state by comparing thesignal value to a threshold value; and performing a topographicalfeature analysis on at least one of a first set of voxels representingthe first disease state and a second set of voxels representing thesecond disease state.
 2. The method of claim 1, wherein the medicalimage is a phasic classification map.
 3. The method of claim 1, whereinthe medical image is a parametric response map.
 4. The method of claim1, wherein the medical image is kinetic parameter map.
 5. The method ofclaim 1, wherein the medical image is a diffusion image, a perfusionimage, a permeability image, a normalized image, a spectroscopic image,and a quantified image.
 6. The method of claim 1, wherein the imagesource is selected from the group consisting of magnetic resonanceimaging, computed tomography, positron emission tomography, ultrasound,single-photon emission computed tomography, and two-dimensional planarx-ray.
 7. The method of claim 1, wherein the topographical feature isselected from the group consisting of surface area, mean curvaturelength, the Euler-Poincare characteristic, and a condensed descriptor ofaggregation.
 8. A computer-implemented method of analyzing a sampleregion of lung tissue to determine the condition of the sample region,the method comprising: receiving, using a medical imaging device, afirst image data set of the sample region during inspiration, the firstimage data set comprising a first plurality of voxels each characterizedby a signal value; receiving, using the medical imaging device, a secondimage data set of the sample region during expiration, the second imagedata set comprising a second plurality of voxels each characterized by asignal value; deformably registering the first image data set and thesecond image data set to produce a co-registered image data set thatcomprises a plurality of co-registered voxels, wherein each of theco-registered voxels includes the signal value of the voxel associatedwith the first image data set, and the signal value of the voxelassociated with the second image data set; performing a first thresholdanalysis on the co-registered voxels to identify a first set ofco-registered voxels indicating a first disease state; performing asecond threshold analysis on the co-registered voxels to identify asecond set of co-registered voxels indicating a second disease state;and performing a topographical feature analysis on at least one of thefirst set of co-registered voxels and the second set of co-registeredvoxels.
 9. The method of claim 8, wherein the first set of co-registeredvoxels comprises co-registered voxels with an inspiration signal valueof greater than a threshold value, indicating the absence ofemphysematous tissue, and an expiration signal value of less than athreshold value, indicating the presence of air-trapping.
 10. The methodof claim 9, wherein the second set of co-registered voxels comprisesco-registered voxels with an inspiration signal value of less than athreshold value, indicating the presence of emphysematous tissue, and anexpiration signal value of greater than a threshold value, indicatingthe absence of air-trapping.
 11. The method of claim 10, wherein thetopographical feature analysis quantifies at least one feature selectedfrom the group consisting of surface area, mean curvature length, theEuler-Poincare characteristic, and a condensed descriptor ofaggregation.
 12. The method of claim 8, wherein the topographicalfeature analysis quantifies at least one feature selected from the groupconsisting of surface area, mean curvature length, the Euler-Poincarecharacteristic, and a condensed descriptor of aggregation.
 13. Themethod of claim 8, wherein the medical image device is selected from thegroup consisting of a magnetic resonance imaging device, computedtomography device, positron emission tomography device, ultrasounddevice, single-photon emission computed tomography device, andtwo-dimensional planar x-ray device.
 14. A computer-based method foranalyzing a parametric response map comprising: receiving a first set ofparametric measurement data for a tissue region, the first set ofparametric measurement data comprising a plurality of voxels; receivinga subsequent set of parametric measurement data for the tissue region,the subsequent set of parametric measurement data comprising a pluralityof voxels; deformably registering the subsequent set of parametricmeasurement data with the first set of parametric response data;identifying the voxels within the tissue region, on a voxel-by-voxelbasis, by at least one classification based on a change in parametricmeasurement data, using a defined threshold for each classification,wherein each of the changes is determined by comparing voxels of thesubsequent set of parametric measurement data to the voxels of the firstset of parametric measurement data; and performing at least onetopographical feature analysis of the voxels for at least one of theclassifications.
 15. The method of claim 14, wherein the topographicalfeature analysis quantifies at least one voxel based on surface area.16. The method of claim 14, wherein the topographical feature analysisquantifies at least one voxel based on mean curvature length.
 17. Themethod of claim 14, wherein the topographical feature analysisquantifies at least one voxel based on the Euler-Poincarecharacteristic.
 18. The method of claim 14, wherein the topographicalfeature analysis quantifies at least one voxel based on a condenseddescriptor of aggregation.
 19. The method of claim 14, wherein theparametric measurement data is displayed as a phasic classification map,a parametric response map, a kinetic parameter map, a diffusion image, aperfusion image, a permeability image, a normalized image, aspectroscopic image, and a quantified image.
 20. The method of claim 14,wherein the parametric measurement data is retrieved from a medicalimage selected from the group consisting of a diffusion image, aperfusion image, a permeability image, a normalized image, aspectroscopic image, a kinetic parameter map, a quantified image, aparametric response map, and a phasic classification map.